MonitorPay Launches the First MCP Server for Bank Account Verification
AI agents are starting to move real money — scheduling supplier payments, onboarding vendors, and approving bank-detail changes with little human oversight. Today MonitorPay is shipping the verification layer that keeps that agentic workflow safe: the first Model Context Protocol (MCP) server purpose-built for company bank account verification. Any MCP-compatible AI assistant can now confirm an account, match a payee, and check the company behind it in real time — before a single payment leaves the building.
What we launched — and why "first" matters
The Model Context Protocol is the open standard that lets an AI assistant discover and call external tools through a single, consistent interface, instead of every developer wiring up a bespoke integration. It has quickly become the way agents connect to the outside world. Plenty of banks and payment networks have started shipping MCP servers for moving money — checking balances, initiating transfers, reading transactions.
What did not exist until today is an MCP server built for the one job that has to happen before money moves: verifying that the bank account, the payee, and the company are real and connected. That is the gap MonitorPay just closed. To our knowledge, no dedicated payment-verification provider has shipped one — which makes MonitorPay the first to bring account verification, payee matching and company checks to AI agents as native, callable tools.
MonitorPay's MCP server turns "verify this company's bank account" into a tool your AI agent can call itself — with a structured, auditable answer in under a second.
What is an MCP server, in plain terms?
The Model Context Protocol is an open standard — introduced by Anthropic and now supported across the major AI tools — for connecting AI assistants to outside tools and data through one common interface. Think of it as a USB-C port for AI: instead of a custom cable for every tool, there's one standard plug. Before MCP, giving an assistant a new capability meant a bespoke integration; with MCP, the assistant just connects to a server and discovers what it can do.
An MCP server is the thing on the other end of that plug. It publishes a set of tools the assistant can call — for MonitorPay, that's verify_account, match_payee, check_ownership, kyb_lookup and the rest. The AI tool you use — Claude, ChatGPT, Cursor, or your own agent — is the client: it connects to the server, sees the available tools, and calls them when a task needs one. MonitorPay runs a remote (hosted) server, so there's nothing to install — you get a server URL and an access token, and you point your client at it.
How to connect the MonitorPay MCP server
Because MCP is model-agnostic, the same server works in every compatible client — you configure it once per tool, not once per model. Exact menu labels shift between app versions, but the shape is the same everywhere: add a server, paste the MonitorPay URL, authenticate.
| AI tool | How to connect it |
|---|---|
| Claude | Settings → Connectors → Add custom connector, paste the MonitorPay server URL, and authenticate. Works in Claude desktop and web. |
| ChatGPT | Turn on Connectors / Developer mode in settings and add the MonitorPay MCP server URL — or, for builders, pass it as an MCP tool in the OpenAI API. |
| Cursor | Settings → MCP → Add new server (or add the entry to ~/.cursor/mcp.json) with the server URL and your auth header. |
| VS Code, Windsurf & other MCP clients | Add the server to the client's MCP config file — same URL, same tools, discovered automatically. |
| Your own agents (SDKs) | Point your MCP client library at the server URL; the tools are listed and callable with no bespoke wrapper. |
MonitorPay issues your server URL and access token during onboarding, since the server sits in the payment path and every deployment is scoped and authenticated.
What the MonitorPay MCP can do
The server exposes MonitorPay's verification stack as a set of discrete tools an agent can call in natural language or as structured function calls. Each returns a clean JSON result with a confidence score and a reason code — the kind of output an agent can actually reason over, not a screenshot it has to interpret.
| Agent tool | What it does | Typical trigger |
|---|---|---|
| verify_account | Confirms an IBAN or local account is valid, active and routable, and checks it against the account holder on record. | "Is this vendor's account real before I schedule the run?" |
| match_payee | Compares the name you're paying against the legal account holder — fuzzy matching, transliteration, confidence score. | "Does the name on the invoice match the account?" |
| check_ownership | Confirms whether the account belongs to the expected party and whether the holder is an individual or business — sourced from banking and government records. | "Does this account belong to the company I'm paying?" |
| verify_vat | Validates a VAT number and returns the official registered company name and status. | "Is this VAT number live and does it match?" |
| kyb_lookup | Pulls registry-sourced company data: legal name, registration number, status, directors, shareholders and UBO. | "Verify the business behind this payment." |
| monitor_account | Registers an account for continuous monitoring and returns webhook alerts when status or ownership changes. | "Watch this supplier and tell me if anything changes." |
Because these are MCP tools, the agent decides when to call them. A finance copilot drafting a payment run can verify every payee without a human writing a single line of integration code. The same tools work in a chat window, inside an ERP copilot, or in a fully autonomous back-office agent — one protocol, everywhere the agent runs.
See it in Claude: verifying a supplier's bank account
Here's the flow that matters most — a supplier asks to change their payout account, the single highest-risk request in accounts payable. With the MonitorPay MCP server connected, an assistant can check the new account against the supplier itself, before anyone approves the change. This is what that conversation looks like.
A supplier, Acme GmbH, just emailed asking us to send all future payments to a new account: IBAN DE89 3704 0044 0532 0130 00. Can you verify it belongs to them before I approve the change?
Let me verify that account against Acme GmbH before you approve anything.
Called MonitorPay MCP → verify_account"iban_valid": true, "account_status": "active", "bank": "Deutsche Bank", "country": "DE", "name_match": { "result": "match", "score": 0.98 }, "risk": "low", "reason_code": "NAME_MATCH", "recommendation": "pass"
The IBAN is a real, active Deutsche Bank account, and the account holder matches Acme GmbH (result: match, score 0.98). The account and the payee line up.
Representative example. The IBAN shown is a public documentation test value, not a live account.
The same call, for your developers
Under the hood it's a standard MCP tool call over JSON-RPC — the assistant discovers the tool and the arguments; you don't build a client.
{
"method": "tools/call",
"params": {
"name": "verify_account",
"arguments": {
"iban": "DE89370400440532013000",
"expected_name": "Acme GmbH"
}
}
}
{
"iban_valid": true,
"account_status": "active",
"name_match": { "result": "match", "score": 0.98 },
"risk": "low",
"reason_code": "NAME_MATCH",
"recommendation": "pass"
}
Illustrative field names and values. name_match.result returns one of match, no_match, or cannot_verify — MonitorPay confirms whether the name matches, it does not return the identity of the account holder. Exact schema is confirmed during onboarding.
A second look: the company behind the payment
Verification isn't only "is this account real." Before onboarding a new supplier or releasing a first payment, the same assistant can pull the registered company behind the payment and reason over it — using the kyb_lookup tool.
We're about to send our first payment to a new supplier, Meridian Trading Ltd. Tell me more about the company behind this payment and flag any risks before we release it.
Pulling the registered company and its ownership now.
Called MonitorPay MCP → kyb_lookup"legal_name": "Meridian Trading Ltd", "registration_number": "15542098", "country": "GB", "status": "active", "incorporation_date": "2026-05-02", "industry": "Wholesale, non-specialised", "vat": { "status": "valid" }, "directors": 1, "ubo": { "verified": true, "count": 1 }
Meridian Trading Ltd is a real, active UK company with a valid VAT number and a verified beneficial owner — so it exists and checks out on paper. Two things stand out, though: it was incorporated about two months ago, and it has a single director and a single UBO under a generic wholesale classification — a profile that overlaps with shell entities used for invoice fraud.
Representative example. Company details are illustrative — MonitorPay returns the factual registry data; the risk read is the assistant reasoning over it.
The use cases: where an agent needs to verify
Verification is only useful at the moment a decision is being made. These are the workflows where teams are already pointing agents — and where an unverified account is a direct route to loss.
Agentic accounts payable
An AP agent building the weekly payment run calls verify_account and match_payee on every line before it schedules a single transfer.
Supplier & customer onboarding
An onboarding bot runs kyb_lookup and check_ownership the moment a new vendor submits details — no queue, no analyst backlog.
Bank-detail change requests
The highest-risk workflow in AP. A copilot verifies the new account against the company of record before the change is ever approved.
Fraud & risk investigation
A risk agent triaging a flagged payment calls check_ownership to confirm who controls the destination account before release.
Payment-run pre-flight
Before a batch leaves the TMS, an agent bulk-verifies thousands of payees in one pass and quarantines the ones that fail.
AI-native fintech products
Builders shipping payment copilots and treasury agents get a verification primitive off the shelf, instead of building rails per country.
A verification checkpoint your agent can call before money moves
MonitorPay checks a payment against real banking infrastructure and government registries in a single step: is the account valid and active, does the payee name match the legal account holder, and is the company behind it registered and correctly owned. The MCP server hands that same checkpoint to your AI agents as callable tools — real-time, cross-border, and logged for audit. It does not initiate or hold funds; it tells your agent whether the destination is safe.
Why it matters now
Two things are happening at once. Agents are being handed more of the payment workflow, and payment fraud is increasingly engineered to defeat the exact manual checks agents are replacing. Vendor email compromise, authorised push payment fraud and bank-detail change scams all work by getting a legitimate-looking instruction in front of whoever approves the payment. When "whoever approves" becomes an autonomous agent, the check has to be built into the agent's own decision loop — not sitting in a separate portal a human was supposed to open.
An agent that can schedule a payment but cannot verify the account is a faster path to a fraudulent transfer, not a safer one. Speed without verification just moves bad money more efficiently.
An MCP server closes that loop. The verification isn't a place the agent has to remember to visit; it's a tool the agent holds and calls as part of doing the task. And because the standard is model-agnostic, the same MonitorPay tools work whether your stack runs on one AI provider today and a different one next quarter.
The benefits, concretely
| Benefit | What changes for your team |
|---|---|
| No integration project | Connect the MCP server and the agent discovers the tools. No SDK build, no field mapping, no per-country plumbing. |
| Verification in the decision loop | The check happens where the payment decision is made, not in a portal someone forgot to open. |
| One protocol, any model | Model-agnostic by design — swap or add AI providers without rewriting your verification layer. |
| Real-time & cross-border | Direct verification across 49+ markets plus registry-sourced KYB on 600M+ companies, in under a second. |
| Audit-ready by default | Every call is logged with a result and reason code — the evidence trail compliance and SOX controls need. |
| Ownership, not just format | Goes beyond "is this IBAN well-formed" to "does this account belong to the company you think you're paying." |
That last point is the one that separates real verification from a validity check. A correctly formatted IBAN tells you nothing about who owns the account. There are plenty of company red flags a bank account match alone won't catch — which is exactly why the MCP exposes ownership and KYB tools alongside the account check.
How to get started
The MonitorPay MCP server is available now for teams building agentic finance and onboarding workflows. Setup is short, but it isn't self-serve yet — because it sits in the payment-approval path, every deployment is provisioned with its own authenticated endpoint and scoped access. In practice, getting live means three things: we issue your server endpoint and credentials, you add them to your MCP-compatible client, and you point your agent at the tools. If you already use the MonitorPay API, the MCP server runs on the same verification engine — same coverage, same data, exposed as agent tools.
The fastest way to see whether it fits your stack is a short call: tell us which client and workflow you're running, and we'll walk through the setup and coverage for your markets.
Book a call to connect the MCP server
We'll set up your authenticated endpoint, confirm the client you're using, and get your agent verifying accounts — usually the same week. Bring your use case; we'll bring the coverage map.
Bulk, API, MCP, or the online platform
However your team works, the same verification and company intelligence is available: send a file for bulk verification of thousands of accounts, wire up the REST API, hand the tools to your agents over MCP, or run one-off checks in the online platform with full audit logs.
Frequently asked questions
What is an MCP server for bank account verification?
An MCP (Model Context Protocol) server exposes a capability to AI agents as a set of standardised, callable tools. MonitorPay's MCP server exposes bank account verification, payee name matching, account ownership, VAT checks and KYB lookups — so an AI assistant can verify a company's bank account itself, in real time, without a custom integration.
Is MonitorPay really the first to launch a bank account verification MCP?
Yes — to our knowledge, MonitorPay is the first dedicated payment-verification provider to ship an MCP server. Several banks and payment networks have released MCP servers for moving money, but none of the specialist account-verification providers had exposed verification, payee matching and company checks to AI agents as native tools before this launch.
Which AI assistants and agents can use the MonitorPay MCP server?
Any MCP-compatible client. Because the Model Context Protocol is an open, model-agnostic standard, the same MonitorPay tools work across compatible AI assistants, ERP and finance copilots, and custom autonomous agents — without rewriting your verification layer if you change AI providers.
What can the MonitorPay MCP server actually verify?
It confirms whether a bank account is valid, active and routable; whether the payee name matches the legal account holder (match, no match, or cannot verify); whether the account belongs to the expected party; whether a VAT number is live and matches; and the registry-sourced company details behind the payment, including directors, shareholders and ultimate beneficial owners.
Does the MCP server move or hold money?
No. MonitorPay is a verification layer, not a payment processor. The MCP server tells your agent whether a destination account and the company behind it are safe to pay — it does not initiate, hold or settle funds. That separation is deliberate: verification is the checkpoint before your existing rails move the money.
How fast is verification through the MCP server?
Median response time is under one second per check, and the underlying engine scales to millions of checks per day. That speed is what makes it usable inside an agent's decision loop and for pre-flighting large payment runs before they leave your treasury system.
What countries and companies are covered?
Direct bank account and payee verification spans 49+ markets — including the EU and UK via the VoP and CoP schemes and direct verification in major markets across the Americas, Asia and the Middle East — backed by registry-sourced company data covering 200+ government registries and 600M+ companies for KYB and ownership checks.
How is this different from just calling the REST API?
The API is ideal when your own code makes the call. The MCP server is for when an AI agent makes the call: the agent discovers the tools, decides when to use them, and reads structured results — with no bespoke integration. Both run on the same verification engine, so coverage and data are identical.
Is verification through the MCP server auditable and compliant?
Yes. Every check is logged with a result and reason code, giving you an audit trail for compliance and internal controls such as SOX. Because the agent's verification step is recorded, you can show exactly what was checked, when, and what the outcome was — which is difficult to evidence when checks happen in a separate portal.
How do I get started with the MonitorPay MCP server?
Connect the MCP server to your MCP-compatible client, point your agent at the tools, and start verifying the same day. You can also access the same verification through bulk file upload, the REST API, or the online platform — get in touch to set up access for your workflow.
